permits <- get_egis_table(db = "housing", table = "tbl_Permit")

2019-03-14 19:56:39: Retrieving table housing.tbl_Permit
Error in ogrInfo(dsn = dsn, layer = layer, encoding = encoding, use_iconv = use_iconv,  : 
  Cannot open layer

First need to join up the real property data (Open Baltimore) the sales data (provided by Steve, and with deed dates from January 1, 2010 through October 2018) so we have a neighborhood for as many sales as we can.

sales <- sales %>% rename(sales.block = Block, sales.lot = Lot)
Error in .f(.x[[i]], ...) : object 'Block' not found
real.prop <- real.prop %>%
  mutate(real.block.clean = gsub("^0+", "", real.block),
         real.lot.clean = gsub("^0+", "", real.lot))
sales <- sales %>%
  mutate(sales.block.clean = gsub("^0+", "", sales.block),
         sales.lot.clean = gsub("^0+", "", sales.lot))
sales <- sales %>%
  left_join(real.prop, 
            by = c("sales.block.clean" = "real.block.clean",
                   "sales.lot.clean" = "real.lot.clean")
            )
sales %>% count(is.na(real.block), is.na(real.lot))

1,153 sales didn’t match to a block-lot in the real property table, which means that the block-lot jointly was not in the real prop table.

Also, there are about 16,000 properties in the real prop table that don’t have a neighborhood.

real.prop %>% count(is.na(neighborhood))

So after joining we end up with 9,428 sales that don’t have a neighborhood.

sales %>% count(!is.na(neighborhood))

The real property table also gives if it is principal residence or not, so we’ll also filter for the sales that are for principal residences.

sales %>% count(rescode)

Distribution of city-wide 2018 sales prices:

sales %>%
  filter(year(deed.date) == 2018) %>%
  ggplot(aes(`Sales Price`)) +
  geom_histogram() +
  theme_iteam_google_docs() +
  xlim(c(0, 500000))

quantile(sales$`Sales Price`, 0.85)
   85% 
275000

Bring in permit data

permits %>% glimpse()
Observations: 836,816
Variables: 45
$ ID_Permit        <int> 375642224, 375642225, 375642226, 375642227, 37...
$ csm_caseno       <fct> 000000001, 000000002, 000000003, 000000004, 00...
$ csm_plan_year    <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_plans_number <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_description  <fct> issued through building inspection, install ga...
$ csm_expr_date    <dttm> 1994-06-06, 1994-06-04, 1993-06-06, 1994-06-0...
$ csm_finaled_date <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ csm_issued_date  <dttm> 1993-09-30, 1993-06-14, 1993-06-14, 1993-06-1...
$ csm_name_first   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_name_last    <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_name_mi      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_projname     <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_recd_by      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_recd_date    <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ csm_status       <fct> EXP, EXP, EXP, EXP, EXP, EXP, EXP, EXP, EXP, E...
$ csm_frozen       <fct> N, N, N, N, N, N, N, N, N, N, F, N, N, N, N, N...
$ csm_auto_cond    <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_updateby     <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_updated      <dttm> 2005-08-07, 2005-08-07, 2005-08-07, 2005-08-0...
$ csm_projno       <fct> 000000001, 000000002, 000000003, 000000004, 00...
$ prc_avp_no       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ csm_target_date  <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
$ case_type        <fct> COM, COM, COM, COM, COM, COM, COM, COM, COM, C...
$ PLANADDRESS      <fct> 0000 COUNTER, 1067 CAMERON ROAD, 4266 CLYDESDA...
$ prc_parcel_no    <fct> 9948 948, 5142 034, 3575C010, 5164 022, 3355 0...
$ com_type_work    <fct> OTH, OTH, AA, OTH, OTH, OTH, OTH, OTH, OTH, AA...
$ com_sprinklers   <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ com_existing_use <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_type_work    <fct> OTH, OTH, AA, OTH, OTH, OTH, OTH, OTH, OTH, AA...
$ csm_use          <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ PlansNum         <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ csm_st_name      <fct> COUNTER, CAMERON ROAD, CLYDESDALE AVE, KENILWO...
$ csm_st_number    <fct> 0000, 1067, 4266, 5313, 2518, 4217, 3527, 0720...
$ csm_st_pfx       <fct> NA, NA, NA, NA, NA, NA, NA, NA, E, NA, NA, NA,...
$ prc_block_no     <fct> 9948 , 5142, 3575C, 5164, 3355, 5749, 5555, 76...
$ prc_lot          <fct> 948, 034, 010, 022, 010, 017, 194, 036, 048, 0...
$ prc_neighborhood <fct> NA, CAMERON VILLAGE, MEDFIELD, KENILWORTH PARK...
$ PlanURL          <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ prc_hi_dist      <int> NA, 570, 540, 570, 672, 480, 470, 980, 551, 62...
$ csm_cost         <dbl> 0, 2000, 2290, 1400, 6600, 5800, 4500, 5350, 7...
$ csm_mastno       <fct> 000000001, 000000002, 000000003, 000000004, 00...
$ BlockLot         <fct> 9948 948, 5142034, 3575C010, 5164022, 3355010,...
$ csm_id           <fct> 200243175721, 200243175722, 200243175723, 2002...
$ Applicant        <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
$ Lessee           <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
permits %>% count(csm_status)
permits %>% count(csm_type_work)

Neighborhood Summary Table, 2015-2017

meet.criteria <- sales %>%
  filter(year(deed.date) %in% c(2015, 2016, 2017),
         !is.na(neighborhood),
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  nrow

We have 16112 samples to work with that are in 2015-2017, have a neighborhood, were an arms-length sale, and are the principal residence.

sales.summary.15_17.by.hood <- sales %>%
  filter(year(deed.date) %in% c(2015, 2016, 2017),
         !is.na(neighborhood),
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  group_by(neighborhood) %>%
  summarise(hood.n = n(),
            hood.mean = mean(`Sales Price`),
            hood.median = median(`Sales Price`),
            hood.std = sqrt(sum((`Sales Price`-hood.mean)^2/(hood.n-1))),
            hood.95th = quantile(`Sales Price`, probs = .95),
            hood.98th = quantile(`Sales Price`, probs = .98),
            hood.99th = quantile(`Sales Price`, probs = .99))
sales.summary.15_17.by.hood

Which neighborhoods have less than 20 sales meeting the criteria?

sales.summary.15_17.by.hood %>%
  filter(hood.n < 20)

84 neighborhoods have less than 20 sales meeting the criteria. We’ll exclude them going forward so we have a reasonable sample size.

# Join the summaries to the neighborhood boundaries
hoods@data <- hoods@data %>% 
  left_join(sales.summary.15_17.by.hood,
            by = c("label" = "neighborhood"))

98th Percentile

Criteria & Results

sales.hood.98th <- sales %>%
  left_join(sales.summary.15_17.by.hood,
            by = c("neighborhood" = "neighborhood")) %>%
  filter(year(deed.date) == 2018,
         hood.n >= 20,
         `Sales Price` >= hood.98th,
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  arrange(neighborhood)
result.sales <- nrow(sales.hood.98th)

There are 167 sales that meet the following criteria:

  • Deed date was between January 1, 2018 and October 5, 2018
  • Arms-length sale
  • Principal residence
  • Neighborhood had at least 20 sales
  • 98th percentile for sales prices for their neighborhood.

(If this yield isn’t high enough we can bump it down to the 95th percentile.)

sales.hood.98th$long <- lapply(sales.hood.98th$location.coordinates, function(x) x[1]) %>% unlist()
sales.hood.98th$lat <- lapply(sales.hood.98th$location.coordinates, function(x) x[2]) %>% unlist()
sales.hood.98th.geo <- sales.hood.98th %>% filter(!is.na(long))
  
sales.hood.98th.geo <- SpatialPointsDataFrame(
  sales.hood.98th.geo %>% select(long, lat), 
  sales.hood.98th.geo,
  proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
sales.hood.98th.geo <- 
  spTransform(
    sales.hood.98th.geo, 
    CRSobj = CRS("+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
    )

Map

library(htmltools)
hoods.labels <- paste0(
  hoods$label,
  "<br>Median Sales, 2015-2017: ", as.character(hoods$hood.median)
  
)
sale.labels <- paste0(
  sales.hood.98th.geo$`House #`, " ",
  sales.hood.98th.geo$`Street Name`, " ",
  sales.hood.98th.geo$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.hood.98th.geo$`Sales Price`),
  "<br>New Owner: ", sales.hood.98th.geo$new.owner
)
leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.hood.98th.geo, 
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))

Full list

sales.hood.98th

Detect jumps

#look for temporal jumps in prices

99th Percentile

Criteria & Results

sales.hood.99th <- sales %>%
  left_join(sales.summary.15_17.by.hood,
            by = c("neighborhood" = "neighborhood")) %>%
  filter(year(deed.date) == 2018,
         hood.n >= 20,
         `Sales Price` >= hood.99th,
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  arrange(neighborhood)
result.sales <- nrow(sales.hood.99th)

There are 167 sales that meet the following criteria:

  • Deed date was between January 1, 2018 and October 5, 2018
  • Arms-length sale
  • Principal residence
  • Neighborhood had at least 20 sales
  • 99th percentile for sales prices for their neighborhood.

(If this yield isn’t high enough we can bump it down to the 95th percentile.)

sales.hood.99th$long <- lapply(sales.hood.99th$location.coordinates, function(x) x[1]) %>% unlist()
sales.hood.99th$lat <- lapply(sales.hood.99th$location.coordinates, function(x) x[2]) %>% unlist()
sales.hood.99th.geo <- sales.hood.99th %>% filter(!is.na(long))
  
sales.hood.99th.geo <- SpatialPointsDataFrame(
  sales.hood.99th.geo %>% select(long, lat), 
  sales.hood.99th.geo,
  proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
sales.hood.99th.geo <- 
  spTransform(
    sales.hood.99th.geo, 
    CRSobj = CRS("+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
    )

Map

library(htmltools)
hoods.labels <- paste0(
  hoods$label,
  "<br>Median Sales, 2015-2017: ", as.character(hoods$hood.median)
  
)
sale.labels <- paste0(
  sales.hood.99th.geo$`House #`, " ",
  sales.hood.99th.geo$`Street Name`, " ",
  sales.hood.99th.geo$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.hood.99th.geo$`Sales Price`),
  "<br>New Owner: ", sales.hood.99th.geo$new.owner
)
leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.hood.99th.geo, 
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))
emily.list <- c("4011 BARRINGTON",
                "DORCHESTER",
                "2319 MONTICELLO")
sales.hood.98th.geo@data %>% 
  filter(grepl(paste(emily.list, collapse="|"), propertyaddress))

In Middle Neighborhood, 99th Percentile for Neighborhood, Over $250k, Over $10k Permit Activity

The following criteria are used below:

permits.recent <- permits %>% 
  filter(csm_issued_date >= "2017-01-01") %>%
  mutate(permit.block.clean = gsub("^0+", "", prc_block_no),
         permit.lot.clean = gsub("^0+", "", prc_lot))
permits.recent.summary <- permits.recent %>%
  group_by(permit.block.clean, permit.lot.clean) %>%
  summarise(permit.count = n(),
            permit.total.value = sum(csm_cost, na.rm = T))
mid.hoods <- hmt.hood %>% filter(`Predominant Code Ignoring Non-Residential` %in% c("D", "E", "F", "G", "H"))
sales.99th.mid.hood <- subset(sales.hood.99th.geo, tolower(neighborhood) %in% tolower(mid.hoods$Neighborhood))
sales.99th.mid.hood.over.250k <- subset(sales.99th.mid.hood,
                                        `Sales Price` > 250000)
sales.99th.mid.hood.over.250k@data %>% nrow

[1] 45
sales.99th.mid.hood.over.250k@data <- sales.99th.mid.hood.over.250k@data %>%
  left_join(permits.recent.summary, 
            by = c("sales.block.clean" = "permit.block.clean",
                   "sales.lot.clean" = "permit.lot.clean"))

Further filter for permit value totals over $10,000.

Results in 27 properties.

sales.99th.mid.hood.over.250k.10k.permit@data %>% nrow

[1] 27
mid.hoods.geo <- subset(hoods, 
                        tolower(label) %in% tolower(mid.hoods$Neighborhood))
mid.hoods.labels <- paste0(
  mid.hoods.geo$label,
  "<br>Median Sales, 2015-2017: ", as.character(mid.hoods.geo$hood.median)
  
)
sale.labels <- paste0(
  sales.99th.mid.hood.over.250k.10k.permit$`House #`, " ",
  sales.99th.mid.hood.over.250k.10k.permit$`Street Name`, " ",
  sales.99th.mid.hood.over.250k.10k.permit$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.99th.mid.hood.over.250k.10k.permit$`Sales Price`),
  "<br>New Owner: ", sales.99th.mid.hood.over.250k.10k.permit$new.owner,
  "<br>Permits Issued from 2017-2018: ", sales.99th.mid.hood.over.250k.10k.permit$permit.count,
  "<br>Total Permit Value from 2017-2018: ", sales.99th.mid.hood.over.250k.10k.permit$permit.total.value
)
leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addPolygons(data = mid.hoods.geo, 
              weight = 2, 
              #color = "black",
              opacity = 0.0,
              fillOpacity = .2,
              fillColor = iteam.colors[3],
              label = ~lapply(mid.hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.99th.mid.hood.over.250k.10k.permit, 
                   color = iteam.colors[1],
                   opacity = 1,
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))

Full List

---
title: "Recent Sales Outliers"
author: "Justin Elszasz, Mayor's Office of Innovation"
email: "justin.elszasz@baltimorecity.gov"
date: "Thursday, February 28, 2019"
output:
  html_notebook:
    code_folding: hide
    fig_height: 5
    fig_width: 10
    toc: yes
    toc_depth: 2
---

```{r setup, include = FALSE, echo = FALSE, message = FALSE, cache = TRUE}
knitr::opts_chunk$set(echo = FALSE, warning = F, message = F, include = T,
                                 fig.width = 10, fig.height = 5)
```


```{r}
source("../src/00_initialize.R")
sales <- load_sales_data(load.cache = T)

library(readxl)
library(RSocrata)
library(sp)
library(leaflet)

real.prop.url <- "https://data.baltimorecity.gov/resource/6act-qzuy.json"
real.prop <- read.socrata(real.prop.url, app_token = VARS$SOCRATA_TOKEN)

hoods <- get_neighborhood_boundaries()
hmt <- load_block_group_data(load.cache = T)
hmt.hood <- read_excel("../data/raw/hmt/HMT by Neighborhood 2017.xlsx")

conn.gis <- odbcDriverConnect(
  paste0(
    'driver={ODBC Driver 13 for SQL Server};',
    'server=', VARS$EGIS_SERVER, 
    ';uid=', VARS$EGIS_SERVER_USER,
    ';pwd=',VARS$EGIS_SERVER_PWD, 
    ';database=housing;trusted_connection=No')
)

permits <- sqlFetch(conn.gis, "housing.tbl_Permit")
```

First need to join up the real property data ([Open Baltimore]([http://data.baltimorecity.gov/Financial/Real-Property-Taxes/27w9-urtvto)) the sales data (provided by Steve, and with **deed dates from January 1, 2010 through October 2018**) so we have a neighborhood for as many sales as we can.

```{r}
sales <- sales %>% rename(sales.block = Block, sales.lot = Lot)
real.prop <- real.prop %>% rename(real.block = block, real.lot = lot)
```

```{r}
real.prop <- real.prop %>%
  mutate(real.block.clean = gsub("^0+", "", real.block),
         real.lot.clean = gsub("^0+", "", real.lot))

sales <- sales %>%
  mutate(sales.block.clean = gsub("^0+", "", sales.block),
         sales.lot.clean = gsub("^0+", "", sales.lot))
```

```{r}
sales <- sales %>%
  left_join(real.prop, 
            by = c("sales.block.clean" = "real.block.clean",
                   "sales.lot.clean" = "real.lot.clean")
            )
```

```{r}
sales %>% count(is.na(real.block), is.na(real.lot))
```

1,153 sales didn't match to a block-lot in the real property table, which means that the block-lot jointly was not in the real prop table. 

Also, there are about 16,000 properties in the real prop table that don't have a neighborhood. 

```{r}
real.prop %>% count(is.na(neighborhood))
```

So after joining we end up with 9,428 sales that don't have a neighborhood.

```{r}
sales %>% count(!is.na(neighborhood))
```

The real property table also gives if it is principal residence or not, so we'll also filter for the sales that are for principal residences.

```{r}
sales %>% count(rescode)
```

Distribution of city-wide 2018 sales prices:

```{r}
sales %>%
  filter(year(deed.date) == 2018) %>%
  ggplot(aes(`Sales Price`)) +
  geom_histogram() +
  theme_iteam_google_docs() +
  xlim(c(0, 500000))
```

```{r}
quantile(sales$`Sales Price`, 0.85)
```
 
# Bring in permit data

```{r}
permits %>% glimpse()
```

```{r}
permits %>% count(csm_status)
```

```{r}
permits %>% count(csm_type_work)
```

# Neighborhood Summary Table, 2015-2017

```{r}
meet.criteria <- sales %>%
  filter(year(deed.date) %in% c(2015, 2016, 2017),
         !is.na(neighborhood),
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  nrow
```

We have `r meet.criteria` samples to work with that are in 2015-2017, have a neighborhood, were an arms-length sale, and are the principal residence.

```{r}
sales.summary.15_17.by.hood <- sales %>%
  filter(year(deed.date) %in% c(2015, 2016, 2017),
         !is.na(neighborhood),
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  group_by(neighborhood) %>%
  summarise(hood.n = n(),
            hood.mean = mean(`Sales Price`),
            hood.median = median(`Sales Price`),
            hood.std = sqrt(sum((`Sales Price`-hood.mean)^2/(hood.n-1))),
            hood.95th = quantile(`Sales Price`, probs = .95),
            hood.98th = quantile(`Sales Price`, probs = .98),
            hood.99th = quantile(`Sales Price`, probs = .99))

sales.summary.15_17.by.hood  
```

Which neighborhoods have less than 20 sales meeting the criteria?

```{r}
sales.summary.15_17.by.hood %>%
  filter(hood.n < 20)
```

84 neighborhoods have less than 20 sales meeting the criteria. We'll exclude them going forward so we have a reasonable sample size.

```{r}
# Join the summaries to the neighborhood boundaries
hoods@data <- hoods@data %>% 
  left_join(sales.summary.15_17.by.hood,
            by = c("label" = "neighborhood"))
```

# 98th Percentile

## Criteria & Results

```{r}
sales.hood.98th <- sales %>%
  left_join(sales.summary.15_17.by.hood,
            by = c("neighborhood" = "neighborhood")) %>%
  filter(year(deed.date) == 2018,
         hood.n >= 20,
         `Sales Price` >= hood.98th,
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  arrange(neighborhood)

result.sales <- nrow(sales.hood.98th)
```

**There are `r result.sales` sales that meet the following criteria:**

- Deed date was between January 1, 2018 and October 5, 2018
- Arms-length sale
- Principal residence
- Neighborhood had at least 20 sales
- 98th percentile for sales prices for their neighborhood.

(If this yield isn't high enough we can bump it down to the 95th percentile.)


```{r}
sales.hood.98th$long <- lapply(sales.hood.98th$location.coordinates, function(x) x[1]) %>% unlist()

sales.hood.98th$lat <- lapply(sales.hood.98th$location.coordinates, function(x) x[2]) %>% unlist()
```


```{r}
sales.hood.98th.geo <- sales.hood.98th %>% filter(!is.na(long))
  
sales.hood.98th.geo <- SpatialPointsDataFrame(
  sales.hood.98th.geo %>% select(long, lat), 
  sales.hood.98th.geo,
  proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))

sales.hood.98th.geo <- 
  spTransform(
    sales.hood.98th.geo, 
    CRSobj = CRS("+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
    )

```

## Map

```{r}
library(htmltools)

hoods.labels <- paste0(
  hoods$label,
  "<br>Median Sales, 2015-2017: ", as.character(hoods$hood.median)
  
)

sale.labels <- paste0(
  sales.hood.98th.geo$`House #`, " ",
  sales.hood.98th.geo$`Street Name`, " ",
  sales.hood.98th.geo$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.hood.98th.geo$`Sales Price`),
  "<br>New Owner: ", sales.hood.98th.geo$new.owner
)


leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.hood.98th.geo, 
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))
```

## Full list

```{r}
sales.hood.98th 
```

# Detect jumps

```{r}
#look for temporal jumps in prices
```

# 99th Percentile

## Criteria & Results

```{r}
sales.hood.99th <- sales %>%
  left_join(sales.summary.15_17.by.hood,
            by = c("neighborhood" = "neighborhood")) %>%
  filter(year(deed.date) == 2018,
         hood.n >= 20,
         `Sales Price` >= hood.99th,
         `How Conveyed` == 1,
         !grepl("NOT", rescode)) %>%
  arrange(neighborhood)

result.sales <- nrow(sales.hood.99th)
```

**There are `r result.sales` sales that meet the following criteria:**

- Deed date was between January 1, 2018 and October 5, 2018
- Arms-length sale
- Principal residence
- Neighborhood had at least 20 sales
- 99th percentile for sales prices for their neighborhood.

(If this yield isn't high enough we can bump it down to the 95th percentile.)


```{r}
sales.hood.99th$long <- lapply(sales.hood.99th$location.coordinates, function(x) x[1]) %>% unlist()

sales.hood.99th$lat <- lapply(sales.hood.99th$location.coordinates, function(x) x[2]) %>% unlist()
```


```{r}
sales.hood.99th.geo <- sales.hood.99th %>% filter(!is.na(long))
  
sales.hood.99th.geo <- SpatialPointsDataFrame(
  sales.hood.99th.geo %>% select(long, lat), 
  sales.hood.99th.geo,
  proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))

sales.hood.99th.geo <- 
  spTransform(
    sales.hood.99th.geo, 
    CRSobj = CRS("+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
    )

```

## Map

```{r}
library(htmltools)

hoods.labels <- paste0(
  hoods$label,
  "<br>Median Sales, 2015-2017: ", as.character(hoods$hood.median)
  
)

sale.labels <- paste0(
  sales.hood.99th.geo$`House #`, " ",
  sales.hood.99th.geo$`Street Name`, " ",
  sales.hood.99th.geo$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.hood.99th.geo$`Sales Price`),
  "<br>New Owner: ", sales.hood.99th.geo$new.owner
)


leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.hood.99th.geo, 
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))
```

```{r}
emily.list <- c("4011 BARRINGTON",
                "DORCHESTER",
                "2319 MONTICELLO")

sales.hood.98th.geo@data %>% 
  filter(grepl(paste(emily.list, collapse="|"), propertyaddress))
```

# In Middle Neighborhood, 99th Percentile for Neighborhood, Over $250k, Over $10k Permit Activity

The following criteria are used below:

- Deed date was between January 1, 2018 and October 5, 2018
- Arms-length sale
- Principal residence
- Neighborhood had at least 20 sales
- 99th percentile for sales prices for their neighborhood.
- Sale price over $250,000
- Permits issued between 2017-2018
- Total permit value at least $10,000

```{r}
permits.recent <- permits %>% 
  filter(csm_issued_date >= "2017-01-01") %>%
  mutate(permit.block.clean = gsub("^0+", "", prc_block_no),
         permit.lot.clean = gsub("^0+", "", prc_lot))

permits.recent.summary <- permits.recent %>%
  group_by(permit.block.clean, permit.lot.clean) %>%
  summarise(permit.count = n(),
            permit.total.value = sum(csm_cost, na.rm = T))
```

```{r}
mid.hoods <- hmt.hood %>% filter(`Predominant Code Ignoring Non-Residential` %in% c("D", "E", "F", "G", "H"))

sales.99th.mid.hood <- subset(sales.hood.99th.geo, tolower(neighborhood) %in% tolower(mid.hoods$Neighborhood))

sales.99th.mid.hood.over.250k <- subset(sales.99th.mid.hood,
                                        `Sales Price` > 250000)
```

```{r}
sales.99th.mid.hood.over.250k@data %>% nrow
```


```{r}
sales.99th.mid.hood.over.250k@data <- sales.99th.mid.hood.over.250k@data %>%
  left_join(permits.recent.summary, 
            by = c("sales.block.clean" = "permit.block.clean",
                   "sales.lot.clean" = "permit.lot.clean"))
```

Further filter for permit value totals over $10,000.

```{r}
sales.99th.mid.hood.over.250k.10k.permit <- subset(sales.99th.mid.hood.over.250k, permit.total.value >= 10000)
                                                   
```

Results in 27 properties.

```{r}
sales.99th.mid.hood.over.250k.10k.permit@data %>% nrow
```


```{r}

mid.hoods.geo <- subset(hoods, 
                        tolower(label) %in% tolower(mid.hoods$Neighborhood))



mid.hoods.labels <- paste0(
  mid.hoods.geo$label,
  "<br>Median Sales, 2015-2017: ", as.character(mid.hoods.geo$hood.median)
  
)

sale.labels <- paste0(
  sales.99th.mid.hood.over.250k.10k.permit$`House #`, " ",
  sales.99th.mid.hood.over.250k.10k.permit$`Street Name`, " ",
  sales.99th.mid.hood.over.250k.10k.permit$Suffix, 
  "<br>Sale Price in 2018: ", 
  as.character(sales.99th.mid.hood.over.250k.10k.permit$`Sales Price`),
  "<br>New Owner: ", sales.99th.mid.hood.over.250k.10k.permit$new.owner,
  "<br>Permits Issued from 2017-2018: ", sales.99th.mid.hood.over.250k.10k.permit$permit.count,
  "<br>Total Permit Value from 2017-2018: ", sales.99th.mid.hood.over.250k.10k.permit$permit.total.value
)


leaflet() %>%
  setView(lng = -76.6, lat = 39.3, zoom = 11) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(data = hoods, 
              weight = 2, 
              
              color = "black",
              opacity = 0.5,
              fillOpacity = 0, 
              label = ~lapply(hoods.labels, HTML)) %>%
  addPolygons(data = mid.hoods.geo, 
              weight = 2, 
              #color = "black",
              opacity = 0.0,
              fillOpacity = .2,
              fillColor = iteam.colors[3],
              label = ~lapply(mid.hoods.labels, HTML)) %>%
  addCircleMarkers(data = sales.99th.mid.hood.over.250k.10k.permit, 
                   color = iteam.colors[1],
                   opacity = 1,
                   radius = 2,
                   label = ~lapply(sale.labels, HTML))
```

## Full List

```{r}
sales.99th.mid.hood.over.250k.10k.permit@data
```



